Short-term wind power prediction based on LSSVM–GSA model

Yuan, Xiao; Chen, Chen; Yuan, Yanbin; Huang, Yuehua; Tan, Qiaoyun

doi:10.1016/j.enconman.2015.05.065

Cited by 235 publications

(97 citation statements)

References 45 publications

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“…Salcedo-Sanz et al [15] present the application of evolutionary-based approaches to optimize the hyperparameters in Support Vector Machines (SVM) and discuss the performance of evolutionary SVR algorithm for wind speed prediction in a wind farm located at the south of Spain. References [16,17] study the performance of hourly ahead wind speed forecasting by LSSVM, in which the typical kernel function (linear, polynomial, and Gaussian kernels) and the related parameters on the forecasting accuracy are investigated. An adaptive robust methodology (Bayesian model averaging) has been developed to forecast hourly wind speed forecasting at two North Dakota sites [18].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

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Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

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Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

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“…The findings demonstrated that the hybrid approach based on wavelet decomposition with LSSVM significantly outperformed the hybrid artificial neural network (ANN)-based methods. Yuan [13] established a LSSVM model in the light of gravitational search algorithm (GSA) for short-term output power prediction of a wind farm. Compared with the back propagation (BP) neural network and support vector machine (SVM) model, the simulation results indicated that the GSA-LSSVM model had higher accuracy for short-term output power prediction.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the wealth of studies on wind speed prediction, there has been less research looking at wind power generation forecasting. The approaches of these studies can be classified into three categories: time series models [4][5][6][7][8], artificial intelligent algorithm models [9][10][11][12][13][14][15][16][17] and time-series artificial intelligent algorithm models [18]. Most of these approaches utilize time series analysis models, including vector autoregressive (VAR) models [4,5], autoregressive moving average (AMRA) models, and autoregressive integrated moving average (ARIMA) models.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, artificial neural network (ANN) [9,10] and least squares support vector machine (LSSVM) [11][12][13][14] are perceived to be highly effective methods in the field of wind power forecasting. Guo [11] successfully developed a hybrid seasonal auto-regression integrated moving average and LSSVM model to forecast the mean monthly wind speed.…”

Section: Introductionmentioning

confidence: 99%

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Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm

Peng

2016

Energies

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Abstract:Regarding the non-stationary and stochastic nature of wind power, wind power generation forecasting plays an essential role in improving the stability and security of the power system when large-scale wind farms are integrated into the whole power grid. Accurate wind power forecasting can make an enormous contribution to the alleviation of the negative impacts on the power system. This study proposes a hybrid wind power generation forecasting model to enhance prediction performance. Ensemble empirical mode decomposition (EEMD) was applied to decompose the original wind power generation series into different sub-series with various frequencies. Principal component analysis (PCA) was employed to reduce the number of inputs without lowering the forecasting accuracy through identifying the variables deemed as significant that maintain most of the comprehensive variability present in the data set. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by bat algorithm (BA) was established to forecast those sub-series extracted from EEMD. The forecasting performances of diverse models were compared, and the findings indicated that there was no accuracy loss when only PCA-selected inputs were utilized. Moreover, the simulation results and grey relational analysis reveal, overall, that the proposed model outperforms the other single or hybrid models.

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“…A hybrid approach based on the combination between least square support vector machine and gravitational search algorithms is proposed [10].…”

Section: Introductionmentioning

confidence: 99%

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Sun

Zhang

2018

Mathematical Problems in Engineering

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A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.

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Short-term wind power prediction based on LSSVM–GSA model

Cited by 235 publications

References 45 publications

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

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